Method for determining a drivable area

ABSTRACT

In one aspect, the present disclosure is directed at a computer implemented method for determining a drivable area in front of a host vehicle. According to the method, a region of interest is monitored in front of the host vehicle by at least two sensors of a detection system of the host vehicle. The region of interest is divided into a plurality of areas via a computer system of the host vehicle, and each area of the plurality of areas is classified as drivable area, non-drivable area or unknown area via the computer system based on fused data received by the at least two sensors.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to European Patent Application Number20171491.2, filed Apr. 27, 2020, the disclosure of which is herebyincorporated by reference in its entirety herein.

BACKGROUND

The present disclosure relates to a method for determining a drivablearea in front of a host vehicle.

Advanced driver assistance systems (ADAS) have been developed to supportdrivers in order to drive a host vehicle more safely and comfortably. Inorder to perform properly and due to safety reasons, the environment infront of a host vehicle needs to be monitored e.g. in order to determinea collision free space in a lane in front of the host vehicle.

In order to determine such a collision free space, objects in front ofthe host vehicle are usually detected e.g. by RADAR or vision sensors ofthe host vehicle. The collision free space is usually represented by itsboundary, e.g. a polygon, enclosing an area between the host vehicle andone or more detected objects.

Furthermore, a so called occupancy grid may be used by applications ofthe advanced driver assistance systems. The occupancy grid comprises aplurality of cells for the area in front of the host vehicle, whereinfor each cell of the occupancy grid information is available whether itis occupied by a detected obstacle or not. The inverse of the occupancygrid may be regarded as a non-blocked area representing the collisionfree space.

A drawback of both concepts, i.e. of the conventional representation ofthe free space and of the occupancy grid, is missing furtherinformation, e.g. regarding drivability, for the region in front of thehost vehicle. If certain parts of this region are identified ascollision free or as not occupied, this does not imply automaticallythat these parts of the region are indeed drivable by the host vehicle.There might be e.g. some abnormality within the lane in front of thehost vehicle which may not be identified as a barrier via the RADAR orvision sensors of the host vehicle.

The conventional representation of the collision free space and theoccupancy grid provide information regarding detected objects only, butthey are not intended to provide further information regarding theproperties of an area being regarded as collision free. Furthermore,both concepts are not able to distinguish between an unknown area forwhich information provided by the vehicle sensors is not yet availableor not reliable, and an area being regarded as “negative” since it isdefinitely blocked, e.g. by a barrier.

A further drawback of the conventional representation of the collisionfree space is that no further information is available beyond thelocation of the detected object. Furthermore, the detected object may bea preceding vehicle moving e.g. with approximately the same velocity asthe host vehicle. Due to the movement of the preceding vehicle, theactual free space being available in front of the host vehicle might belarger than the detected free space. If the detected free space is usedonly, assistance systems of the host vehicle might be deactivatedunnecessarily since the free space in front of the host vehicle isassumed to be too narrow for a proper performance of these systems.

Regarding the conventional occupancy grid, the grid size, i.e. thenumber of cells of the grid, needs to be very large in many cases inorder to cover the environment in front of the host vehicle properly. Ifthe longitudinal and lateral extension of the occupancy grid needs to beincreased, the number of cells being required increases quadratically.

Accordingly, there is a need to have methods and vehicle-based systemswhich are able to validate areas in front of a host vehicle.

SUMMARY

The present disclosure provides a computer implemented method, avehicle-based system, a computer system and a non-transitory computerreadable medium according to the independent claims. Embodiments aregiven in the subclaims, the description and the drawings.

In one aspect, the present disclosure is directed at a computerimplemented method for determining a drivable area in front of a hostvehicle. According to the method, a region of interest is monitored infront of the host vehicle by at least two sensors of a detection systemof the host vehicle. The region of interest is divided into a pluralityof areas via a computer system of the host vehicle, and each area of theplurality of areas is classified as drivable area, non-drivable area orunknown area via the computer system based on fused data received by theat least two sensors.

Since fused data of at least two sensors are used for classifying theareas in front of the vehicle, the method provides detailed informationregarding drivability for the region of interest into which the hostvehicle is going to move. The two sensors may include a vision sensorlike a camera and/or a RADAR system or a LIDAR system which are alreadyavailable in the host vehicle. Therefore, the method may be implementedat low cost since no additional hardware components are required.

Data from different kinds of sensors may be used for the classificationof the areas. Moreover, the data which are fused by the method for theclassification may be pre-validated in order to provide e.g. informationregarding detected objects and/or regarding properties of the lane infront of the host vehicle. That is, a flexible and modular approachregarding the data provided by the at least two sensors may beimplemented for the method. In addition to an obstacle free space infront of the vehicle, further information is provided by the method dueto the classification of areas within and beyond the obstacle freespace. The areas classified as drivable areas may be regarded asrepresenting a drivable surface in front of the host vehicle. Such adrivable surface provided by the method may constitute a necessary inputfor autonomous driving level 3 or higher.

Due to the classification of areas as drivable areas, non-drivable areasor unknown areas, the safety of the host vehicle is improved incomparison to determining an obstacle free space in front of the hostvehicle only. Furthermore, the method distinguishes between areas whichare definitely regarded as non-drivable and areas which are regarded asunknown so far. The unknown area may be further validated by additionalsensor data in order to clarify their status. However, the unknown areasmay be assessed as risky regarding drivability momentarily.

The method may comprise one or more of the following features.

Via the detection system an obstacle free space may be detected in frontof the host vehicle and road information may be determined. An area ofthe plurality of areas may be classified based on the free space andbased on the road information. Detecting the obstacle free space infront of the host vehicle may comprise detecting at least one object infront of the host vehicle and tracing a trail of the object in order toidentify whether the at least one object is a moving object or a staticobject.

An area of the plurality of areas may be classified as non-drivable areaif the area is blocked at least partly by an identified static object.Furthermore, it may be determined whether an identified moving object isa target vehicle preceding the host vehicle. An area of the plurality ofareas may be classified as drivable area if the area is disposed in alane between the target vehicle and the host vehicle. A current velocityof the target vehicle may be determined via the detection system, and anextension of the obstacle free space may be estimated via the computersystem of the host vehicle-based on the current velocity of the targetvehicle and based on a prediction of an emergency braking distanceand/or an emergency steering distance of the target vehicle.

A road model may be generated based on the road information determinedby the detection system of the host vehicle and/or based on at least onepredefined map being stored via the computer system. An abnormality maydetected at or within a surface of a lane in front of the host vehicle,and an area of the plurality of areas may be classified as non-drivablearea if the area is at least partly occupied by the abnormality. Areliability value may be determined for each of the plurality of areasbased on a fusion of data provided by the at least two sensors, and eacharea may be classified based on the reliability value.

Dividing the region of interest into the plurality of areas may compriseforming a dynamic grid in front of the host vehicle. The dynamic gridmay comprise a plurality of cells and may be based on the course of alane being determined in front of the host vehicle via the detectionsystem. Forming the dynamic grid in front of the host vehicle maycomprise detecting an indicator for the course of the lane in front ofthe host vehicle via the detection system, determining, via the computersystem of the host vehicle, a base area based on the indicator for thecourse of a lane, and defining, via the computer system, the pluralityof cells by dividing the base area in longitudinal and lateraldirections with respect to the host vehicle.

A common boundary of the areas being classified as drivable areas may bedetermined, and a plurality of nodes may be defined along the boundary.The nodes may be connected via a polygon in order to generate a convexhull representing a drivable surface in front of the host vehicle.

According to an embodiment, an obstacle free space may be detected infront of the host vehicle and road information may be determined, bothby using the detection system of the host vehicle including the at leasttwo sensors. A certain area of the plurality of areas in front of thehost vehicle may be classified as drivable area, non-drivable area orunknown area based on the obstacle free space and based on the roadinformation. The road information may comprise information regarding thesurface of the lane in which the host vehicle is momentarily drivingand/or information regarding road delimiters or lane markings. Since theclassification of an area is performed based on the further roadinformation in addition to the obstacle free space, detailed andreliable information regarding drivability may be available for theassistance systems of the host vehicle.

Detecting the obstacle free space in front of the host vehicle maycomprise detecting at least one object in front of the host vehicle andtracing a trail of the object. By this means the at least one object maybe identified as a moving object or a static object. That is, inaddition to a position of an object in front of the host vehicle, thisobject is categorized as moving or static by tracing its trail. Hence,additional information regarding the status of movement of the objectmay be used for the classification of areas in front of the host vehicleas drivable areas, non-drivable areas or unknown areas.

An area of the plurality of areas may be classified as non-drivable areaif the area is blocked at least partly by an identified static object.If an area is regarded as “blocked”, this area is not only regarded asoccupied by any unspecified object. In addition, it may be determined bythe at least two sensors if the static object is an actual barrier whichblocks the driving course and should be avoided by the host vehicle, orif it does not really constitute an obstacle for driving.

Furthermore, it may be determined whether an identified moving object isa target vehicle preceding the host vehicle. An area of the plurality ofareas may be classified as drivable area if the area is disposed in alane between the target vehicle and the host vehicle. Since the targetvehicle has already traversed certain areas in front of the hostvehicle, these areas may obviously be regarded as drivable areas due tothe “experience” of the target vehicle. Therefore, the assessment ofcertain areas in front of the host vehicle may be facilitated by tracinga target vehicle preceding the host vehicle.

In addition, a current velocity of the target vehicle may be determinedvia the detection system. Based on the current velocity of the targetvehicle and based on a prediction of an emergency braking distanceand/or an emergency steering distance of the target vehicle, anextension of the obstacle free space may be estimated via the computersystem of the host vehicle. That is, the movement of the target vehicleis considered in order to determine an actually available free space infront of the host vehicle. The free space regarded as available for thehost vehicle may therefore not be restricted to the current distancebetween the target vehicle and the host vehicle since the movement ofthe target vehicle is taken into account for an extension of theobstacle free space.

Since some assistance systems of the host vehicle may rely on theobstacle free space and may be deactivated if this obstacle free spaceis too small, these assistance systems may not be deactivatedunnecessarily if the obstacle free space can be extended. On the otherhand, emergency braking and emergency steering are taken into account byrespective distances in order to consider a “worst case” for a changeregarding the status of movement of the target vehicle. The maximumextension of the obstacle free space may therefore be limited by theemergency braking distance and the emergency steering distance in orderto ensure the safety of the target vehicle and of the host vehicle.

According to another embodiment, a road model may be generated based onthe road information determined by the detection system of the hostvehicle and/or based on at least one predefined map being stored via thecomputer system. The road model may be regarded as a fused road modelsince information detected by the at least two sensors of the hostvehicle and information from a predefined map may be used when the roadmodel is generated. The road model may comprise information regardingthe course of a lane in front of the host vehicle, information regardingboundaries of the lane and information regarding the surface of thelane. In addition, a vertical curvature of the lane may be estimated forthe region of interest in front of the host vehicle. Since the roadmodel comprising detailed information about the lane in front of thehost vehicle may be used for classifying the areas in front of the hostvehicle, the reliability of the classification may be improved.

Furthermore, an abnormality may be detected at or within a surface of alane in front of the host vehicle. An area of the plurality of areas maybe classified as non-drivable area if the area is at least partlyoccupied by the abnormality. Examples for such abnormalities may be apothole within the surface of the lane, unexpected obstacles, e.g. dueto lost items from other vehicles, or irregularities of the consistencyof the lane. Such abnormalities may be assessed based on the fused dataof the at least two sensors of the host vehicle. Based on such anassessment, certain areas within the lane in front of the host vehiclemay be classified as non-drivable area.

A reliability value may be determined for each of the plurality of areasbased on a fusion of data provided by the at least two sensors.Classifying an area of the plurality of areas may further be based onthe reliability value. For example, an error analysis may be known foreach sensor of the detection system which may be the basis for an errorestimation for the information provided by each of the sensors. The datafusion for the at least two sensors may provide an error estimation aswell for the fused data. The reliability value may be based on the fusederror analysis. The reliability value may improve the classification ofthe areas, e.g. by defining a threshold for the reliability value inorder to classify an area as drivable area. Conversely, if thereliability value for an area is below a further threshold, this areamay be classified as unknown area.

According to a further embodiment, dividing the region of interest intoa plurality of areas may comprise forming a dynamic grid in front of thehost vehicle. The dynamic grid may include a plurality of cells and maybe based on the course of a lane which is determined in front of thehost vehicle via the detection system. Since the dynamic grid reflectsthe course of the lane in front of the host vehicle, the number of cellsrequired for such a grid may be reduced since the grid is restricted tothe region of interest in front of the host vehicle. Therefore, thecomputational effort for performing the method may be strongly reduced.

Forming the dynamic grid may comprise i) detecting an indicator for thecourse of the lane in front of the host vehicle via the detectionsystem, ii) determining, via the computer system of the host vehicle, abase area based on the indicator for the course of the lane, and iii)defining, via the computer system, the plurality of cells by dividingthe base area in longitudinal and lateral directions with respect to thehost vehicle. The indicator for the course of the lane may include rightand/or left margins of the lane and/or markers for the center of thelane. By this means, straight forward grid based information may beavailable for further applications of the host vehicle after each cellof the dynamic grid may be classified as drivable area, non-drivablearea or unknown area. Depending on the resolution of the grid, verydetailed information may be made available.

A reference line may be defined via the computer system of the hostvehicle along the lane based on the indicator, and the reference linemay be divided into segments. For each of the segments, a respective rowof cells may be defined perpendicularly to the reference line.Generating the dynamic grid may be facilitated by defining such rows ofcells corresponding to the segments of the reference line.

In addition, for each segment two respective straight lines may bedefined perpendicularly to the reference line at a beginning and at anend of the segment, respectively. Each straight line may be divided intoa predefined number of sections, and end points of the respectivesections may define corners of a respective one of the plurality ofcells. Such a definition of the corners for the respective cells of thedynamic grid, i.e. by using the end points of the sections, may furtherfacilitate the generation of the dynamic grid and may therefore reducethe required computational effort.

According to a further embodiment, a common boundary may be determinedfor the areas which are classified as drivable areas. A plurality ofnodes may be defined along this boundary, and the nodes may be connectedvia a polygon in order to generate the convex hull representing adrivable surface in front of the host vehicle. Such a representation ofthe drivable surface may be easily used by further applications fortrajectory and motion planning of the host vehicle. Due to the straightforward representation via the polygon, the computational complexity isreduced for the further applications.

In another aspect, the present disclosure is directed at a vehicle-basedsystem for determining a drivable area in front of a host vehicle. Thesystem comprises a detection system and a computer system of the hostvehicle. The detection system comprises at least two sensors beingconfigured to monitor a region of interest in front of the host vehicle.The computer system is configured to divide the region of interest intoa plurality of areas, and to classify each area of the plurality ofareas as drivable area, non-drivable area or unknown area based on fuseddata received by the at least two sensors.

As used herein, a computer system may include an Application SpecificIntegrated Circuit (ASIC), an electronic circuit, a combinational logiccircuit, a field programmable gate array (FPGA), a processor (shared,dedicated, or group) that executes code, other suitable components thatprovide the described functionality, or a combination of some or all ofthe above, such as in a system-on-chip. The computer system may furtherinclude memory (shared, dedicated, or group) that stores code executedby the processor.

In summary, the system according to the disclosure comprises twosub-systems for performing the steps as described above for thecorresponding method. Therefore, the benefits and advantages asdescribed above for the method are also valid for the system accordingto the disclosure.

The detection system of the host vehicle may comprise a visual systemand/or a RADAR system and/or a LIDAR system being configured to monitorthe environment of the host vehicle. The visual system, the RADAR systemand/or the LIDAR system may already be implemented in the host vehicle.Therefore, the system may be implemented at low cost, e.g. by generatingsuitable software for validating, via the computer system, the dataprovided by the detection system.

In another aspect, the present disclosure is directed at a computersystem, said computer system being configured to carry out several orall steps of the computer implemented method described herein.

The computer system may comprise a processing unit, at least one memoryunit and at least one non-transitory data storage. The non-transitorydata storage and/or the memory unit may comprise a computer program forinstructing the computer to perform several or all steps or aspects ofthe computer implemented method described herein.

In another aspect, the present disclosure is directed at anon-transitory computer readable medium comprising instructions forcarrying out several or all steps or aspects of the computer implementedmethod described herein. The computer readable medium may be configuredas: an optical medium, such as a compact disc (CD) or a digitalversatile disk (DVD); a magnetic medium, such as a hard disk drive(HDD); a solid state drive (SSD); a read only memory (ROM), such as aflash memory; or the like. Furthermore, the computer readable medium maybe configured as a data storage that is accessible via a dataconnection, such as an internet connection. The computer readable mediummay, for example, be an online data repository or a cloud storage.

The present disclosure is also directed at a computer program forinstructing a computer to perform several or all steps or aspects of thecomputer implemented method described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments and functions of the present disclosure aredescribed herein in conjunction with the following drawings, showingschematically:

FIG. 1 depicts a dynamic grid in front of a host vehicle;

FIG. 2 depicts details for determining the dynamic grid as shown in FIG.1 ;

FIG. 3 depicts a classification of areas in front of the host vehicleaccording to the disclosure;

FIG. 4 depicts details for classifying areas in front of the hostvehicle as drivable area;

FIG. 5 depicts details for classifying areas in front of the hostvehicle as non-drivable area; and

FIG. 6 depicts a flow diagram of a method for determining a drivablearea in front of the host vehicle.

DETAILED DESCRIPTION

FIG. 1 schematically depicts a host vehicle 11 including a detectionsystem 13 which is configured to monitor an environment of the hostvehicle 11. The detection system 13 includes at least two sensors, e.g.sensors of a visual system and/or of a RADAR or LIDAR system, formonitoring the environment or a so-called “region of interest” in frontof the host vehicle 11. The host vehicle 11 further includes a computersystem 15 for processing the data provided by the detection system 13.

The host vehicle 11 further comprises a vehicle coordinate system 17which is a Cartesian coordinate system including an x-axis extending ina lateral direction with respect to the vehicle 11 and a y-axisextending in a longitudinal direction in front of the host vehicle 11.As an example for an obstacle limiting a free space in front of the hostvehicle 11, the existence and the position of a target vehicle 19 aredetected by the detection system 13 of the host vehicle 11.

Furthermore, FIG. 1 depicts a schematic representation of a dynamic grid21. The dynamic grid 21 includes a plurality of dynamic cells 23 and isadapted to the course of a lane 25 in front of the host vehicle 11. Indetail, the dynamic grid 21 is defined via the computer system 15 of thehost vehicle 11 for a base area 27 which corresponds to the region ofinterest in front of the host vehicle 11. In order to define the basearea 27, a left margin 29 and the right margin 31 of the lane 25 aredetected by the detection system 13 of the host vehicle 11. Since theleft margin 29 and the right margin 31 limit the lane 25, the left andright margins 29, 31 are used as indicators for the course of the lane25 in front of the host vehicle 11.

As mentioned above, the base area 27 for the dynamic occupancy grid 21is intended to cover the region of interest for the host vehicle 11. Forcovering this region of interest properly, some areas beyond the leftmargin 29 and beyond the right margin 31 are included in the base area27. That is, some parts of adjacent lanes, sidewalks and/or furtherenvironment like ditches may also be relevant for the further movementof the host vehicle 11 and have therefore to be included into the basearea 27. The base area 27 is further divided in a plurality of dynamiccells 23 in order to generate the dynamic occupancy grid 21.

FIG. 2 depicts in detail how the dynamic cells 23 of the dynamicoccupancy grid 21 are generated via the computer system 15 of the hostvehicle 11. A reference line 33 is defined which extends approximatelyin the center of the lane 25 in which the host vehicle 11 and the targetvehicle 19 are driving momentarily. The reference line 33 is representedby a polynomial whose coefficients are derived from an indicator for thecourse of the lane 25 which is measured by the detection system 13 ofthe host vehicle 11, e.g. by measuring the course of the left margin 29and the right margin 31 of the lane 25 as indicators for the course ofthe lane 25.

The reference line 33 represented by the polynomial is divided into aplurality of segments 35 having a constant length A along the referenceline 27. For each segment 35, two straight lines 37 are definedextending perpendicularly to the reference line 33, respectively. Thatis, adjacent segments 35 have a common straight line 37 which delimitsrespective areas from each other extending on both sides of thereference line 33 between the straight lines 37. The straight lines 37are further divided into sections 39 having a constant length δ.Therefore, end points 41 of the respective sections 39 also have aconstant distance δ from each other.

The end points 41 of the sections 39 are used in order to define cornerpoints for a respective dynamic cell 23 (see FIG. 1 ). In detail, twoend points 41 of a section 39 being adjacent to each other and belongingto a first straight line 37 define two corner points of a dynamic cell23, whereas two further end points 41 of a section 39 of the adjacentstraight line 37 having the shortest distances to the first straightline 37 define two further corner points for the dynamic cell 23. Thatis, the four corner points of each dynamic cell 25 are defined byrespective end points 41 of sections 39 belonging to adjacent straightlines 37 and having the shortest distance with respect to each other.

Due to the curvature of the reference line 33, the size of the dynamiccells 23 varies within the dynamic occupancy grid 21, as can berecognized in FIG. 1 . In addition, the length of the segments 35 may bevaried as an alternative along the reference line 31. For example, closeto the host vehicle 11 a short length of the segments 35 may be used,whereas the length of the segments 35 may increase when their distanceincreases with respect to the host vehicle 11.

In the example as shown in FIG. 2 , each segment 35 defines a row ofdynamic cells 23, wherein this row extends perpendicularly to thereference line 33. If a predefined number of cells 23 is used for eachrow of cells 23 belonging to a certain segment 35, a constant lateralwidth of the dynamic occupancy grid 21 is defined corresponding to aconstant lateral extension of the base area 27 corresponding to andcovering the region of interest in front of the host vehicle 11.

Alternatively, the number of cells 23 for each row may be adjusted tothe curvature of the lane 25 and the reference line 33. In detail, agreater number of cells 23 may be considered on a first side to whichthe reference line 23 is curved, e.g. on the right side as shown inFIGS. 1 and 2 , whereas a smaller number of cells 23 is taken intoaccount on the second side from which the reference line 33 departs.Such a situation is shown in FIG. 1 in which more cells 23 are presentat the “inner side” of the lane 25 beyond the right margin 31, whereasless cells 23 are considered at the “outer side” of the left margin 29of the lane 25.

FIG. 3 depicts the host vehicle 11 and a part of a road including thelane 25 in which the host vehicle 11 is currently driving. The base area27 corresponding to the region of interest in front of the host vehicle11 is covered by the dynamic grid 21 which is described in detail incontext of FIGS. 1 and 2 . As can be seen in FIG. 3 , the dynamic grid21 comprising a plurality of cells 23 follows the course of the lane 25and covers the base area 27 or region of interest in front of the hostvehicle 11.

In detail, the dynamic grid 21 covers the lane 25 in which the hostvehicle 11 is currently driving, an adjacent lane on the left side ofthe host vehicle 11, and a further region on the right side of the hostvehicle 11. This region on the right side does not belong to the roadanymore, but it might be of interest for some of the assistance systemsof the host vehicle 11. Two further lanes on the left side of the hostvehicle 11 are provided for oncoming traffic and are therefore notcovered by the base area 27 restricting the dynamic grid 21.

As mentioned above, the detection system 13 of the host vehicle 11includes at least two sensors, e.g. visual sensors and/or RADAR sensorsand/or LIDAR sensors, which are configured to monitor the environment ofthe host vehicle 11. Via these sensors additional information isprovided which allows classifying the cells 23 of the dynamic grid 21 asdrivable areas 43, non-drivable areas 45 or unknown areas 47.

For example, one of the at least two sensors of the detection system 13determines the position of road delimiters 49 on the left and rightsides of the host vehicle 11, respectively. Therefore, the cells 23 ofthe dynamic grid 21 in which the road delimiters 49 are disposed areclassified as non-drivable areas 45.

The same sensor and/or another sensor of the at least two sensors isconfigured to detect objects in the environment of the host vehicle 11in order to determine an obstacle free space. As a result, the cells 23which are located within the hatched area as shown in FIG. 3 areregarded as obstacle free and are classified as drivable areas 43. Thehatched area in front of the host vehicle 11 may also be regarded asdrivable surface.

For a part of the cells 23 no reliable information is available for atleast one of the two sensors of the detection system 13, e.g. sincethese cells 23 are hidden behind an obstacle or out of range for thespecific sensor. Hence, these cells 23 for which no reliable informationis available by the relevant sensor of the at least two sensors areclassified as unknown areas 47. As can be recognized in FIG. 3 , thecells beyond the road limiter 49 on the right side of the host vehicle11 are classified as unknown areas 47. In addition, cells belonging tothe lane 25 and the adjacent lane on the left side of the host vehicle11 are also classified as unknown areas 47 if these cells 23 are e.g.out of range of a visual sensor of the detection system 13.

FIG. 4 depicts details for classifying cells 23 of the dynamic grid 21as drivable areas. A subset of the cells 23 of the dynamic grid 21 isshown on the left side of FIG. 4 together with objects 51 which aredetected by a sensor 53, e.g. a visual sensor or a RADAR or LIDARsensor, of the detection system 13 of the host vehicle 11. An enlargedsection shown on the right side of FIG. 4 depicts one of the objects 51and the cells 23 surrounding this object 51. The detected objects 51delimit the obstacle free space in front of the host vehicle 11.

The cells 23 which are located between the sensor 53 and one of theobjects 51 and which are not covered by any part of an object 51 areregarded as obstacle free and drivable. In other words, the cells 23 arelocated within an instrumental field of view of the sensor 53 and arefree of any of the detected objects 51 in order to be classified asdrivable areas 43. Conversely, the cells 23 which are covered at leastpartly by one of the detected objects 51 or which are located behind oneof the objects 51 and are therefore “hidden from view” are classified asunknown areas 47. This is due to the fact that the objects 51 might bemovable objects, e.g. other vehicles, and therefore the cells 23 whichare covered momentarily by one of the objects 51 may be obstacle freeand therefore drivable at a later instant of time. Currently, however,these cells 23 are occupied by one of the objects 51. Since this statusmight change in the near future, the cells 23 surrounding the object 51and being located behind the objects 51 are regarded as unknown areas47. In the enlarged section on the right side of FIG. 4 , the cells 23classified as drivable areas 43 are shown as hatched areas, whereas thecells 23 which are covered at least partly by the object 51 areclassified as unknown areas 47.

In addition, there are cells 23 on the right and left sides of thesensor 53 which are outside the instrumental field of view of thespecific sensor 53. Since no information is available for these cellsvia the specific sensor 53, these cells are also regarded as unknownareas 47.

FIG. 5 depicts details for classifying cells 23 of the dynamic grid 21as non-drivable areas 45. On the left side of FIG. 5 , the same subsetof cells 23 is shown as on the left side of FIG. 4 . Via a furthersensor 55 of the detection system 13 of the host vehicle 11, a roaddelimiter 49 is detected in a lateral direction with respect to the hostvehicle 11. The road delimiter 49 is shown in the enlarged section onthe right side of FIG. 5 .

The cells 23 of the dynamic grid 21 which are covered at least partly bythe detected road delimiter 49 are classified as non-drivable areas 45.For the further cells 23 which are not covered by any part of the roaddelimiter 49, additional information is available via the sensor 53 (seeFIG. 4 ) according to which these cells 23 can be regarded as obstaclefree. Therefore, these cells 23 are classified as drivable areas 43.

In the enlarged section on the right side of FIG. 5 , the cells 23 whichare classified as non-drivable areas 45 are shown as diagonally hatchedareas. In contrast, the cells 23 being classified as drivable areas 43are not hatched.

As a further condition for classifying the cells 23, an abnormality maydetected at or within a surface of the lane 25 in front of the hostvehicle 11, e.g. via one of the sensors 53, 55 of the detection system13. Such an abnormality may be e.g. a pothole or an item being lost byanother vehicle. The area is classified as non-drivable area 45 if thearea is at least partly occupied by the abnormality.

If an instrumental error is known for the respective sensors 53, 55which are configured to detect different items in the environment of thehost vehicle 11, a combined or fused error can be defined for the fusedinformation provided by both sensors 53, 55. Based on the combined errorfor both sensors 53, 55, a reliability value may be defined forclassifying the cells 23 of the dynamic grid 21.

When a drivable surface has been determined in front of the host vehicle11 by combining the drivable areas 43 (see FIG. 3 ), a boundary of thedrivable surface can be defined including nodes or edges which limit thedrivable surface. These nodes or edges can be connected by a polygon inorder to provide a convex hull representing the drivable surface infront of the host vehicle 11. This provides a straightforwardrepresentation for the drivable surface which can be easily used byfurther applications of the host vehicle 11 which include trajectory andmotion planning features.

FIG. 6 depicts a flow diagram of a method 100 for determining a drivablearea 43 in front of the host vehicle 11. At step 110, a region ofinterest is monitored in front of the host vehicle 11 by the at leasttwo sensors 53, 55 of the detection system 15 of the host vehicle 11.The region of interest corresponds to the base area 27 which is used fordetermining the dynamic grid 21 in front of the host vehicle (see FIGS.1 and 3 )

Next, at step 120 the region of interest or base area 27 is divided intoa plurality of areas via a computer system 15 of the host vehicle 11.The areas may correspond to the cells 23 of the dynamic grid 21, forexample, as shown in FIG. 1 and in FIGS. 3 to 5 .

Thereafter, at step 130 each area of the plurality of areas isclassified as drivable area 43, non-drivable area 45 or unknown area 47(see FIGS. 3, 4 and 5 ) based on fused data received by the at least twosensors 53, 55 via the computer system 15. The area is classified, forexample, based on an obstacle free space in front of the host vehicleand based on road information which are determined via the detectionsystem 13 of the host vehicle.

Based on the road information and/or based on at least one predefinedmap being stored via the computer system, a road model may be generatedwhich is used for classifying the area. In addition, an abnormality maydetected at or within a surface of the lane 25 in front of the hostvehicle 11, e.g. via one of the sensors 53, 55. The area is classifiedas non-drivable area 45 if the area is at least partly occupied by theabnormality.

What is claimed is:
 1. A method, comprising: monitoring, by at least twosensors of a detection system of a host vehicle, a region of interest infront of the host vehicle; dividing the region of interest into aplurality of areas; classifying each area of the plurality of areas as adrivable area, a non-drivable area, or an unknown area; responsive toclassifying an area as a drivable area and determining that a targetvehicle precedes the host vehicle, determining a current velocity of thetarget vehicle; estimating, based on the current velocity of the targetvehicle having a positive value and based on a prediction of anemergency braking distance or an emergency steering distance related tothe target vehicle, an extension of an obstacle free space in front ofthe host vehicle; and controlling the host vehicle based on theestimation of the extension of the obstacle free space in front of thehost vehicle and the classifying of each area.
 2. The method accordingto claim 1, further comprising: detecting, via the detection system, theobstacle free space in front of the host vehicle; determining, via thedetection system, road information; and classifying, based on theobstacle free space and based on the road information, the area of theplurality of areas.
 3. The method according to claim 2, whereindetecting the obstacle free space in front of the host vehiclecomprises: detecting at least one object in front of the host vehicle;and tracing a trail of the object to identify whether the at least oneobject is a moving object or a static object.
 4. The method according toclaim 3, wherein an area of the plurality of areas is classified as anon-drivable area if the area is blocked at least partly by anidentified static object.
 5. The method according to claim 3, furthercomprising: determining whether an identified moving object is thetarget vehicle preceding the host vehicle; and classifying the area ofthe plurality of areas as a drivable area if the area is disposed in alane between the target vehicle and the host vehicle.
 6. The methodaccording to claim 2, further comprising: generating, based on the roadinformation or at least one predefined map, a road model.
 7. The methodaccording to claim 1, further comprising: detecting an abnormality on asurface of a lane in front of the host vehicle; and classifying the areaof the plurality of areas as a non-drivable area if the area is at leastpartly occupied by the abnormality.
 8. The method according to claim 1,further comprising: determining a reliability value for each area of theplurality of areas based on a fusion of data provided by the at leasttwo sensors; and classifying each area based on the reliability value.9. The method according to claim 1, wherein dividing the region ofinterest into the plurality of areas comprises forming a dynamic grid infront of the host vehicle, the dynamic grid comprising a plurality ofcells and is based on a course of a lane being determined in front ofthe host vehicle via the detection system.
 10. The method according toclaim 9, wherein forming the dynamic grid in front of the host vehiclecomprises: detecting, via the detection system, an indicator for thecourse of the lane in front of the host vehicle; determining, based onthe indicator for the course of the lane, a base area; and defining theplurality of cells by dividing the base area in longitudinal and lateraldirections with respect to the host vehicle.
 11. The method according toclaim 9, further comprising: determining a common boundary of the areasbeing classified as drivable areas; and defining a plurality of nodesalong the boundary, the nodes being connected via a polygon to generatea convex hull representing a drivable surface in front of the hostvehicle.
 12. A system, comprising: a detection system comprising atleast two sensors configured to monitor a region of interest in front ofa host vehicle; and a computer system configured to: divide the regionof interest into a plurality of areas; classify each area of theplurality of areas as a drivable area, a non-drivable area, or anunknown area; responsive to classifying an area as a drivable area anddetermining that a target vehicle precedes the host vehicle, determine acurrent velocity of the target vehicle; estimate, based on the currentvelocity of the target vehicle having a positive value and based on aprediction of an emergency braking distance or an emergency steeringdistance related to the target vehicle, an extension of an obstacle freespace in front of the host vehicle; and control the host vehicle basedon the estimation of the extension of the obstacle free space in frontof the host vehicle and on the classifying of the area.
 13. The systemof claim 12, wherein the computer system is further configured to:detect, via the detection system, the obstacle free space in front ofthe host vehicle; determine, via the detection system, road information;and classify, based on the obstacle free space and based on the roadinformation, the area of the plurality of areas.
 14. The systemaccording to claim 13, wherein the computer system is configured todetect the obstacle free space in front of the host vehicle by:detecting at least one object in front of the host vehicle; and tracinga trail of the object to identify whether the at least one object is amoving object or a static object.
 15. The system according to claim 13,wherein the computer system is further configured to: generate, based onthe road information or at least one predefined map being stored via thecomputer system, a road model.
 16. The system according to claim 12,wherein the computer system is further configured to: detect anabnormality on a surface of a lane in front of the host vehicle; andclassify an area of the plurality of areas as a non-drivable area if thearea is at least partly occupied by the abnormality.
 17. The systemaccording to claim 12, wherein the computer system is further configuredto: determine a reliability value for each area of the plurality ofareas based on a fusion of data provided by the at least two sensors;and classify each area based on the reliability value.
 18. The systemaccording to claim 12, wherein the computer system is configured todivide the region of interest into the plurality of areas by: forming adynamic grid in front of the host vehicle, the dynamic grid comprising aplurality of cells and is based on a course of a lane being determinedin front of the host vehicle via the detection system.
 19. Anon-transitory computer readable medium comprising instructions that,when executed, cause a processor to: monitor, via at least two sensorsof a detection system of a host vehicle, a region of interest in frontof the host vehicle; divide the region of interest into a plurality ofareas; classify each area of the plurality of areas as a drivable area,a non-drivable area, or an unknown area; responsive to classifying anarea as a drivable area and determining that a target vehicle precedesthe host vehicle, determine a current velocity of the target vehicle;estimate, based on the current velocity of the target vehicle having apositive value and based on a prediction of an emergency brakingdistance or an emergency steering distance related to the targetvehicle, an extension of an obstacle free space in front of the hostvehicle; and control the host vehicle based on the estimation of theextension of the obstacle free space in front of the host vehicle andthe classifying of each area as a non-drivable area.
 20. Thenon-transitory computer readable medium of claim 19, wherein theinstructions, when executed, cause the processor to divide the region ofinterest into a plurality of areas by: forming a dynamic grid in frontof the host vehicle, the dynamic grid comprising a plurality of cellsand is based on a course of a lane being determined in front of the hostvehicle via the detection system.